Automated driving in road freight transport

Abstract

The freight transport system is expected to face significant changes driven by emerging technologies, increasing transport demand, and the need for rapid decarbonization. Automated driving systems and their application to road freight in the form of driverless trucks is one such technology that may influence the development. Driverless trucks could potentially enable cost efficient, safe, and flexible transport solutions, provided that technical, regulatory, and operational challenges are overcome. However, there is significant uncertainty regarding their development trajectory, future use, and system-level impacts such as changes in transport costs, freight patterns, and mode shifts, as well as their implications for sustainable freight transport.

This thesis explores potential long-term system-level impacts of driverless trucks and implications for planning, policy, and sustainability, with a focus on the Swedish freight transport system. Four objectives are addressed. First, future scenarios for the freight transport system are developed, and an analysis of the Swedish innovation system for driverless trucks is performed. The results suggest that plausible initial deployments of driverless trucks are within confined areas, short-distance repetitive flows, and for highway driving between logistics facilities. The innovation process of driverless trucks is characterized by cooperation among a broad set of actors, and it is possible that driverless trucks will disrupt the value chain of road freight transport.

Second, the potential impacts on road transport costs are modeled, showing that driverless trucks could reduce costs by 20% or more, largely determined by the extent to which total labor costs can be reduced.

Third, system-level impacts are analyzed for a large set of introduction scenarios, using national freight transport modeling. The change in cost structure could lead to increased demand for road transport and shifts from rail and sea to road, which may have implications for infrastructure planning, policymaking, and environmental sustainability. Furthermore, driverless trucks capable of operating on highways and strategically chosen access roads can address a substantial amount of freight demand and generate significant system impacts.

Finally, this thesis explores how model-based analysis of driverless trucks’ cost performance, system-level impacts, and climate policy implications under uncertainty can be enhanced using exploratory modeling and methods for decision-making under deep uncertainty. The application of such methods demonstrates that they can contribute to a broader understanding of potential impacts and policy robustness. Several challenges for their introduction in national transport planning are identified, including the need for more flexible and faster models, and managing fundamental differences in approach compared to the current prediction-based planning paradigm.

This thesis contributes with research on the potential system-level impacts of driverless trucks, which may be of relevance for the freight transport industry as well as planners and policymakers at the national level. The research offers an initial, broad examination of a topic for which literature is scarce. Several areas for future research are identified, including the relationship between driverless trucks, electrification, and freight decarbonization; improving modeling of costs and operations of driverless trucks at the vehicle and fleet levels; as well as developing tools to support exploratory modeling and planning to handle uncertainty about the future.

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